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Exploring thermal and entropic behaviors in nanofluid stagnation point flow with nonlinear dynamics
This study investigates the optimization of heat and mass transfer in nanofluid stagnation point flow by analyzing entropy generation and its underlying physical mechanisms. Nanofluid technology, widely applied in thermal energy storage, and heat exchangers represents a significant advancement in modern thermal systems. While nanofluids enhance heat transfer rates, optimizing thermal conductivity through nanoparticle dispersion remains a key challenge. This work also incorporates the effects of a nonlinear chemical reaction to evaluate its impact on coupled heat and mass transport. The governing nonlinear partial differential equations, including momentum, energy, and concentration expressions, are reduced to a system of coupled ordinary differential equations using local similarity transformations. These equations are solved numerically using a Runge-Kutta scheme in MATLAB. The results, presented through tables and graphs, demonstrate how velocity, temperature, and concentration profiles vary with key physical parameters. Entropy generation is shown to increase with higher porosity, while reductions in slip and Williamson fluid parameters decrease it. Furthermore, the skin friction coefficient increases by approximately 7 % when the magnetic parameter M increases from 0 to 0.5, whereas the Nusselt number decreases by nearly 28.6 % as M increases from 0 to 1. Additionally, the local Sherwood number decreases by approximately 16.7 % when the permeability parameter Kp increases from 0 to 0.3. These findings provide practical insights into enhancing nanofluid based heat and mass transfer systems for engineering applications. 2025 The Authors. -
Biosynthesized carbon quantum dots/g-C3N4/Co3O4 composites for effective methylene blue dye degradation and DFT study
In this study, we aimed to develop a new, efficient photocatalyst, graphitic carbon nitride/carbon quantum dots/cobalt oxide (g-C3N4/CQDs/Co3O4 (CCC)), via a hydrothermal route. The composite was synthesized through a simple hydrothermal method, with the Co3O4 nanoparticles (NPs) systematically varied to 3, 5, and 10 %. The resulting samples are comprehensively characterized using various techniques, including X-ray diffraction (XRD), Raman spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), BrunauerEmmettTeller (BET) surface area analysis, vibrating sample magnetometry (VSM), thermogravimetric analysis (TGA), and ultraviolet-visible (UVVis) spectroscopy. Photocatalytic activity was evaluated using methylene blue (MB) dye under UV light. Among the prepared samples, the 3 % Co3O4 NPs loaded CCC catalyst has shown superior photocatalytic efficiency of 94.5 % within 120 min, which is higher than that of the 5 and 10 % Co3O4 NPs loaded CCC composite and better than that of the pristine materials. The results are obtained for optimized conditions at a concentration of 5 ppm, 0.05 g and pH 10. The 3 % CCC composite has exhibited excellent reusability and stability upto five cycles. Furthermore, Density Functional Theory (DFT) was used to understand the crystal structure and electronic properties of the prepared composite. The results have demonstrate that the novel CCC composite is a promising catalyst for the degradation of MB dye in aqueous solutions and environmental remediation. 2025 Elsevier B.V. -
Novel biogenic CNS@AgNPs hybrid nanostructures for electrochemical detection of sucralose: Experimental and in silico strategies
Carbon, the most abundant and versatile element, has played a significant role in scientific innovations, forming the backbone of material science and nanotechnology. This study presents the first reported simultaneous photogenic synthesis of carbon nanospheres (CNS) integrated with silver nanoparticles (CNS@AgNPs) using Coriander sativum seed extract for sucralose detection. The CNS@AgNPs formation, mediated by oleic acid from the extract, was confirmed with GCMS analysis. The morphology of the CNS@AgNPs was characterized using SEM, TEM, XPS, XRD, Raman, BET, Diffuse Reflectance Spectroscopy (DRS), and Thermogravimetric analysis (TGA). The fabricated GE/Nafion/CNS@AgNPs electrode demonstrated an intense oxidation peak current at +0.7V, with Differential Pulse Voltammetry (DPV) showing a linear response from 2.0 to 14?M, with a LOD and LOQ of 0.2?M and 0.62?M (R2=0.998), respectively. The Density Functional Theory (DFT) studies revealed key mechanistic insights, including the methanol cleavage energy (~3.135נ103eV) and HOMO-LUMO differences between neutral sucralose and its cationic form. Monte Carlo (MC) simulations confirmed favourable adsorption energy (?52.739kcal/mol) with specific binding interactions (3.3578.653 influencing electron transfer pathways. This eco-friendly approach presents the potential of sustainable materials for developing efficient electrochemical sensors for detecting artificial sweeteners in real samples. 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Harnessing immobilized copper salophen complex for the electrochemical synthesis of phenazine
This study introduces a novel approach utilizing a modified electrode, denoted as Cu-PDABA-CFP, as a pivotal catalyst in the electrochemical synthesis of phenazine. Through bulk electrolysis of o-phenylene diamine in an acetonitrile medium, facilitated by lithium perchlorate as the supporting electrolyte, the electrode serves as a fundamental component in this synthetic endeavor. The modification process entails the immobilization of a copper-salophen complex, synthesized in accordance with established literature protocols, onto the electrode surface. Surface characterization of the modified electrode was meticulously conducted to get critical insights into the structural morphology and topographical features of the electrode surface, pivotal for understanding its electrochemical behavior. Concurrently, electrochemical characterization studies were undertaken to evaluate the inherent activity of the modified electrode. To elucidate the intricate electrochemical reaction mechanism underlying the synthetic transformation, an exhaustive screening of reaction conditions was meticulously undertaken. The findings presented herein contribute not only to advancing our fundamental understanding of electrochemical processes but also hold promise for the development of novel electrochemical methodologies with broader applicability in synthetic chemistry and materials science. 2025 Elsevier Ltd -
Ruthenium phosphate-embedded poly-(3,4 diaminobenzoic acid)-based electrode for enhanced sensing of 2,4-dichlorophenol in water samples
2,4-Dichlorophenol (2,4-DCP) is found to have a prevalent application in synthesizing many industrial materials, meanwhile leading to toxicological effects on human health and aquatic life. This work demonstrates the construction of a highly responsive electrochemical sensing platform for 2,4-DCP, based on ruthenium phosphate electrodeposited over poly-(3,4 diaminobenzoic acid)-loaded carbon fiber paper (Ru-Pi/PDABA/CFP). Surface modification of the conducting polymer with Ru-Pi improves electrocatalytic performance by enhancing available electrocatalytic sites and rapid charge transmission channels. The developed electrode was characterized using XRD, XPS, and SEM studies to substantiate the formation of Ru-Pi/PDABA/CFP hybrid material, and electrochemical studies further evidence the improved electrochemical performance upon electrode modification. Cyclic voltammetric studies showcased 2-fold enhanced catalytic activity of Ru-Pi/PDABA/CFP compared to the bare CFP. Differential pulse voltammetric outcome corroborated outstanding electroanalytical metrics towards 2,4-DCP, unveiling an appreciably minimal limit of detection (LOD) of 1.47 nM and a low quantification boundary (LOQ) of 4.37 nM in a wide concentration-response linearity of 5 450 nM. The interferences from foreign substances produced only negligible signal modulations (<4.6 %) on the current amplitude of 2,4-DCP, validating the sensor's excellent selectivity towards the target analyte. Further, the application of Ru-Pi/PDABA/CFP was extended for the 2,4-DCP assay in actual tap and lake water samples. 2025 -
Stepwise hydrothermally synthesized gold nanoparticles supported copper metal-organic frameworks as an impedimetric immunosensor for the ultrasensitive detection of pancreatic cancer
Carbohydrate antigen (CA199) is a frequently used biomarker for detecting and prognosis of pancreatic cancer. Early detection of pancreatic cancer remains a challenge in routine clinical analyses, including imaging techniques such as magnetic resonance imaging, ultrasonography, and computed tomography. There is an urgent urge to develop robust sensors like electrochemical immunosensors that provide low-priced and sensitive biomarker detection. A potential electrochemical immunosensor comprising Au nanoparticles supported on Cu MOF, HKUST-1 (Au@HKUST-1) on screen-printed carbon electrodes (SPCE) was developed via a one-pot stepwise hydrothermal method for the ultralow level detection of CA 199 in human serum using electrochemical impedance spectroscopy (EIS). CA 199 could be detected in a wide range of concentrations, including 0.01 U mL-1 to 35 U mL-1. Au@HKUST-1/SPCE sensor displayed ultralow levels of detection of CA 199 with a limit of detection (LOD) in PBS as 0.17 U mL-1 and serum as 0.08 U mL-1. The limit of quantification of the immunosensor is 0.53 U mL-1 and 0.26 U mL-1 in PBS and serum, respectively. This work also emphasizes the ultrasensitive detection of pancreatic cancer using CA 199 biomarker in serum samples, and achieved a reliable analytical platform for the detection of CA 199 and other biomarkers. 2025 Elsevier Ltd. -
Putting sustainable human resource management and workplace eudaimonic well-being into cross-cultural context
This study examines how sustainable human resource management (HRM) impacts employee work engagement and eudaimonic well-being across cultural contexts that differ on individualism-collectivism dimension. Theoretically, the study draws from Self-Determination Theory (SDT; Ryan & Deci, 2017) and the model of culture fit (Aycan et al., 1999). Using data from 14,502 employees nested in 54 countries working in a variety of positions across different sectors, we found support for our hypothesized modelthat is, sustainable HRM was positively related to employee eudaimonic well-being via enhanced work engagement. The study found that one moderating effectthe relationship between work engagement and eudaimonic well-beingwas stronger in countries that are more individualistic rather than collectivistic. The findings provide support for the universality of the SDT-based approach to understanding employee experiences based on sustainable HRM and cultural variations that inform work-related eudaimonic well-being. Our study advances existing cross-cultural research on sustainable HRM and employee well-being. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
High-speed portrait video segmentation using the hybrid combination of deep-learning models and boundary movement adjustment
As global warming intensifies, the development of energy-efficient Artificial Intelligence (AI) technologies has become crucial. Additionally, the growing demand for on-device AI in smartphones, extended reality devices, and autonomous vehicles necessitates AI systems that can function effectively on low-performance hardware. To address these needs, this study proposes hybrid methods in the field of Portrait Video Segmentation (PVS). Our proposed hybrid models leverage Deep-learning based Segmentation Models (DSMs) and a novel Boundary Movement Adjustment (BMA) process to achieve speed and accuracy balance. The Hybrid Serial Model (HSM) not only accelerates PVS but also improves energy efficiency while maintaining a similar level of accuracy. On the other hand, the Hybrid Parallel Model (HPM) enables high-performance PVS even on low-performance devices, ensuring no video frames are lost during high-speed segmentation processing. Tests conducted on Jetson Nano, Raspberry Pi, and a desktop PC demonstrate the effectiveness of these models, showing improvements in PVS speed while maintaining accuracy close to that of traditional DSMs. HSM increased PVS speed from 15.2 Frames Per Second (FPS) to 25.1 FPS on a desktop PC with a 0.5 % accuracy loss, and from 6.3 FPS to 16.5 FPS on a Jetson Nano with a 1 % loss. HPM reached 30 FPS on a desktop PC with a 0.05 % loss, and 29.7 FPS on a Jetson Nano with a 1 % loss. On the Raspberry Pi, the HPM method improved speed from 2.9 FPS to 29.8 FPS, demonstrating its adaptability for low-performance devices. 2025 Elsevier Ltd -
AI-driven load forecasting and energy management in smart grids using hybrid deep models
Modern power systems are becoming more complex, and integrating renewable energy sources (RES) calls for sophisticated solutions for accurate load forecasting and efficient energy management. To improve forecast accuracy and operational efficiency in smart grids, the research suggests a hybrid deep learning (DL) structure that blends convolutional neural networks (CNN) with long short-term memory (LSTM) systems. The LSTM element records sequential connections within historical energy usage, while the CNN element extracts geographical features from environmental variables such as temperature, humidity, and solar radiation. A comprehensive preprocessing pipeline comprising data cleaning, normalization, and feature selection ensures high-quality inputs for model training. The proposed LSTM-bCNN model is evaluated using a publicly available dataset, and its performance is benchmarked against traditional and contemporary models including ARIMA, SVM, RF, and standalone LSTM. According to findings from experiments, the mixture model obtains the highest R-squared (R) value, the lowest Mean Absolute Error (MAE), and the Root Mean Squared Error (RMSE), confirming its robustness in capturing complex patterns in energy consumption. This research highlights the possible of hybrid DL models in enabling intelligent, adaptive, and resilient energy management systems (EMS) within next-generation smart grids. 2026 Elsevier B.V. -
A robust explainable machine learning pipeline for transformer health index prediction addressing data pathologies and redundancy
Power transformers are critical infrastructure assets where unexpected failures incur severe technical and economic penalties. This study proposes a robust, explainable machine-learning (ML) pipeline for predicting the transformer Health Index (HI) using routinely collected dissolved gas analysis (DGA) and dielectric measurements. To ensure model reliability, the pipeline specifically addresses data pathologiesnamely extreme skewness and heavy tailsusing YeoJohnson transformations, while mitigating multicollinearity through hierarchical correlation clustering (|r| ? 0.85) followed by a Variance Inflation Factor (VIF) screening (VIF ? 5). Four high-performance ensemblesRandom Forest, XGBoost, LightGBM, and CatBoostwere optimized via randomized cross-validation. Experimental results on a dataset of 470 records demonstrate consistent generalization across all models (RMSE ? 0.022), with Random Forest providing superior accuracy (MAPE ? 1.24%). A Taylor diagram confirmed consistent generalization (correlation ? 0.730.78 and matched variance), while residual analysis showed minimal bias. SHAP explanations indicated that dibenzyl disulfide (DBDS) and interfacial tension (Interfacial V) were the most influential positive drivers of HI; water content tended to depress HI; and several gases (e.g., methane, hydrogen, acetylene, CO) contributed positively at higher concentrations. The proposed workflow was robust to skew/heavy tails and multicollinearity, required no feature scaling, and produced transparent, practitioner-ready insights that support condition-based maintenance at fleet scale. 2026 Elsevier B.V. -
Synergistic g-c3n4/v2o5/pani composite for electrochemical energy storage
This work illustrates the synthesis of a ternary hybrid composite (g-C3N4/V2O5/PANI) from graphitic carbon nitride, vanadium pentoxide, and Polyaniline via hydrothermal method followed by in-situ polymerization. Morphological analysis confirms the integration of vanadium pentoxide (V2O5) and polyaniline (PANI) within the interlayer spaces of graphitic materials. The resultant hybrid composite structure facilitates rapid diffusion and ion movement at the electrode-electrolyte interface. Additionally, incorporating V2O5 within a polymer matrix alongside graphitic material generates diverse electrical profiles, enhancing electrochemical performance. The electrochemical characteristics of g-C3N4/V2O5/PANI composites were examined by Cyclic voltammetry (CV), Galvanostatic charge-discharge (GCD), and Electrochemical impedance spectroscopy (EIS). The GCD analysis shows that the g-C3N4/V2O5/PANI composite exhibits a specific capacitance of 880 Fg?1 at a current density of 1 Ag?1, retaining 78 % of its initial capacitance after executing 2000 cycles at 3 Ag?1. Furthermore, a symmetric supercapacitor was constructed using g-C3N4/V2O5/PANI composite material as the electrode, showing a capacitance of 246 Fg?1 when measured at an input current density of 1 Ag?1. This study demonstrates g-C3N4/V2O5/PANI is a potential electrode material for supercapacitor application. 2024 -
Acid functionalized Arachis hypogaea skin based carbon nanosphere as efficacious material for enhanced energy storage
The present introduces a single step approach for enhancing supercapacitor performance by utilizing acid-functionalized porous carbon derived from the inner skin of Arachis hypogaea as a sustainable biomass precursor. Through pyrolysis at 800 C in a nitrogen atmosphere, the resulting porous carbon material demonstrates unique structural and electrochemical behavior as confirmed by FTIR, XRD, Raman spectroscopy, FE-SEM, HR-TEM,EDS,BET analyses. The acid functionalized variant (FAH8) significantly outperformed the non-functionalized carbon (AH8), showing a fourfold increase in specific capacitance. Electrochemical evaluations revealed that FAH8 achieved a high specific capacitance of 273 Fg?1 at 0.25 Ag?1 in 3 M KOH, with an energy density of 22.5 Wh kg?1 and a power density of 125 W kg?1 in a three-electrode setup. The symmetrical CR2032 device of FAH8 exhibited a maximum capacitance of 98 Fg?1 and displayed excellent stability, with 98.5 % efficiency and 97.4 % capacitance retention after 7500 cycles. Notably, the device also delivered a high energy density of 23.17 Wh kg?1 and power density of 325.0 W kg?1. The enhanced performance attributed by the simple acid functionalization highlights the potential of this material in energy storage. Thus, the study not only emphasizes the effective use of low-cost biomass precursors but also provides a straightforward functionalization strategy to boost energy storage capabilities, paving the way for sustainable high-performance supercapacitors. 2025 Elsevier Ltd -
Biowaste-derived hierarchical activated porous carbon with heteroatom-doping (N/S) for efficient symmetrical supercapacitors: A cow urine approach
The continuous accumulation of biowaste in the environment over extended periods can pose considerable ecological challenges. Hence, the conversion of natural biowaste into value-added products is essential. In this study, for the first time, carbon materials derived from cow urine, an animal waste, were explored as potential electrode materials for supercapacitors (SCs). Hierarchical, highly porous carbonaceous materials containing heteroatoms such as N and S were synthesized using a simple, template-free pyrolysis method, involving the direct carbonization of cow urine as a single precursor at 700 C (CCUR-700) and pre-KOH activation of the resulting cow urine deposit pyrolyzed at 700 C (A-CCUR-700) with a removal of inherent mineral salts. The resulting porous carbon materials were then employed as electrode materials for SC applications. The A-CCUR-700 electrode, with its abundant surface functionalities, high specific surface area (2651.7 m2/g), high porosity, good conductivity, and self-doped heteroatoms (N and S), demonstrated better charge storage performance compared to the CCUR-700 electrode. Notably, a two-electrode symmetric SC assembled using the A-CCUR-700 electrode demonstrated an excellent specific capacitance of 165 F/g at a current density of 0.5 A g?1. Furthermore, the A-CCUR-700 symmetric SC device achieved a high energy and power density of 22.9 Wh/kg and 5100 W/kg, respectively, with a capacitance retention of 95.3 % over 5000 cycles. Overall, the results of this study suggest that the synthesis of functionalized carbonaceous materials from cow urine may open up new possibilities for producing inexpensive electrode materials for electrochemical value-added applications. 2025 -
Amide-enriched pod-based carbon nanospheres for enhancing supercapacitor performance: A value-added approach for solid state supercapacitors
The present work involves the fabrication of symmetric solid-state supercapacitors (SSSCs) using amide-functionalized carbon nanospheres (CNS) derived from Magnolia champaca pods, a bio-waste material. The pods were carbonized at temperatures ranging from 400 C to 1000 C, with CNS at 800 C (MC800) showing best electrochemical performance. The synthesized materials, i.e., MC400, MC600, MC800, MC1000, were characterized by techniques such as FESEM, HR-TEM, FTIR, XRD, Raman spectroscopy, and BET. Amide functionalization, achieved through the use of 2,3,4-trifluoroaniline (TFA), enhanced charge storage capacity by improving ion transport and surface interaction, resulting in the functionalized CNS labeled as MC800/COOH-TFA. The electrochemical investigation of the CNS was studied via techniques such as cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) and electrochemical impedance spectroscopy (EIS). The functionalization led to two-fold increase in specific capacitance from 243 Fg?1 to 410 Fg?1 at a current density of 0.25Ag?1 in 3 M KOH. The SSSCs was fabricated using MC800/COOH-TFA with a PVA-KOH gel electrolyte demonstrating a good areal capacitance of 40 mFcm?2 at 1.0 mAcm?2. Moreover, the device exhibited excellent energy density of 5.54 ?Whcm?2 and cycle stability, retaining 71.75 % of its capacitance after 10,000 charge-discharge cycles. The response time of the functionalized sample has been reduced to 2.31 s (MC800/COOH-TFA) from 4.73 s (MC800). These results highlight the potential of amide functionalized CNS in producing efficient, sustainable energy storage devices with improved performance. 2025 Elsevier Ltd -
Phytoremediated nickel-enriched biochar composite for high-performance supercapacitors
Renewable and sustainable high-performance energy storage devices are essential to meet the needs of next-generation power sources. This study explores the use of the hyperaccumulator Dracaena trifasciata (snake plant) grown in manipulated soil (with Nickel) to explore a cost-effective, sustainable phytoremediation technique for synthesizing high-performance biocarbon electrode material. The synthesized Nickel-Biochar (Ni-Biochar) is treated with acid to enhance its processability and is then combined with an optimal amount of Polyaniline (PANI) to improve charge conductivity. The Ni-Biochar/PANI electrode demonstrates excellent electrochemical performance, with a specific capacitance of 638 F g?1 at 0.5 A g?1 in a three-electrode cell and notable stability, retaining 92 % of its capacity after 10,000 cycles. Additionally, the asymmetric supercapacitor made with Ni-Biochar/PANI achieves a specific capacitance of 163 F g?1 in a 3 M KOH solution. The Ragone plot for this device reveals an energy density of 57 Whkg?1 and a power density of 1259 W kg?1. The device also shows outstanding long-term cyclic stability, retaining 90 % of its capacity after 5000 charge-discharge cycles. This high level of performance underscores the potential of utilizing plants as green carbon sources, which can be combined with various metal oxides and conducting polymers to produce hybrid nanomaterials, making them highly promising for sustainable supercapacitor electrode applications. 2025 Elsevier Ltd -
Cobalt oxide intercalated graphitic carbon nitride- polyaniline hybrid architecture for supercapacitors
In this study, a graphitic carbon nitride/Cobalt oxide/Polyaniline (g-C3N4/Co3O4/PANI) ternary nanocomposite was synthesized through an integrated approach combining a simple hydrothermal method with in-situ oxidative polymerization. The binary g-C3N4/Co3O4 and g-C3N4/PANI hybrid composites were also synthesized to elucidate the synergistic effects of the individual components. The structural and morphological analysis confirms the successful formation of binary and ternary composites. The porous architecture of g-C3N4/Co3O4/PANI nanocomposite synergistically combines the pseudocapacitive contributions of Co3O4, the conductive pathways of PANI, and the stabilizing role of g-C3N4, resulting in enhanced surface accessibility and improved electrolyte wettability. Strong interfacial interactions, including ?-? conjugation between g-C3N4 and PANI with Co3O4 induced electrostatic stabilization, ensuring considerable mechanical robustness. Electrochemical assessments reveal that the g-C3N4/Co3O4/PANI composite showcased a remarkable specific capacitance of 1152 F g?1 and 93 % capacitance retention over 5000 galvanostatic charge-discharge cycles. The configured asymmetric supercapacitor (g-C3N4/Co3O4/PANI//activated carbon) delivers superior energy and power densities of 59.1 Wh kg?1 and 2693 W kg?1, respectively. The developed nanocomposite represents a significant advancement in hybrid electrode materials, offering substantial potential for next-generation high-performance energy storage systems. 2025 Elsevier Ltd -
Optimizing supercapacitor electrodes via lithium-induced JahnTeller modulation in CuO
AbstractThe development of advanced electrode materials with superior electrochemical properties is essential to meet the growing demand for efficient energy storage technologies. While surface engineering is common to address this fundamental challenge, the present work shifts the focus from external morphology to internal structural stabilization. Through an integrated experimental and density functional theory (DFT) approach, we demonstrate that a moderate lithium incorporation of 4at. % achieves an optimal balance in CuO properties by suppressing subtle JahnTeller distortions, enhancing crystallite size, narrowing the band gap, and improving both optical and electrical conductivity. X-ray Absorption Spectroscopy (XAS) confirms that Li-ion incorporation increases local symmetry around Cu sites, while EXAFS analysis identifies localized structural disorder associated with dopant substitution. This dual effect stabilizes the CuO lattice while simultaneously creating additional redox-active sites. Electrochemical testing validates this approach, as the optimized 4at. % Li-doped CuO electrode delivers a high specific capacitance of 656F/g at 1 A g?1. The fabricated symmetric supercapacitor device delivers an energy density of ~7Whkg?1 at a power density of ~700Wkg?1, demonstrating the feasibility of Li-doped CuO thin films for supercapacitor applications, although further optimization is required to improve long-term cycling stability. This synergistic experimentaltheoretical framework provides both fundamental insight and practical guidelines for the rational design of doped transition-metal oxides, offering a cost-effective and scalable strategy for next-generation energy storage applications. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Autonomous green vegetable growth monitoring via YOLOv9 and a vine robot with tracked mobility
Urban agriculture is facing shrinking land while demand for food is increasing. The study introduces a vine-like, soft robot for non-destructive tracking of green vegetable development using a tracked mobile platform. Although an inbuilt camera and YOLOv9 object detector classify in real time and generate results in four size categories, very small, small, medium, and large, a flexible tube is everted into dense greenery through a pneumatic eversion process. Sensor fusion and hierarchical control are integrated to enable navigation through the complex canopies of crops with accurate control of pressure and direction, and steering. A field trial found 91% mAP detection accuracy at 38 FPS, accurate vine extension (1.2 m @ 4 cm/s), and stable locomotion over uneven terrain, resulting in constant coverage without harming the plants. The system provides a scalable solution for precision agriculture, enhancing crop inspection, disease diagnosis, and harvest planning through continuous data insights. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Study of heat transfer in a rotating weakly electrically conducting Newtonian fluid: Primary and Kpers-Lortz regimes
In this paper, we study the primary and secondary (Kpers-Lortz) instabilities of rotating RayleighBard convection for a weakly electrically conducting Newtonian fluid with idealistic boundaries. The critical Rayleigh number is obtained for each instability. Fourth-order and ninth-order Lorenz model are derived using the truncated Fourier-Galerkin expansion and the onset of primary and secondary instabilities is studied. Using a non-linear analysis, we derive the expression for the Nusselt number for both primary and secondary instabilities. The analysis reveals that the heat transfer in the case of primary instability is an over-prediction when compared with that of the secondary instability. An increase in the strength of the magnetic field is to delay the onset of primary and secondary instabilities and decrease the heat transfer. These insights advance the understanding of magnetohydrodynamic stability in rotating convective systems and have implications for geophysical and astrophysical fluid dynamics. 2025 Elsevier Masson SAS -
Fractional and memory effects on wave reflection in pre-stressed microstructured solids with dual porosity
The present work investigates the influence of fractional-order derivative and memory-dependent derivative on the behavior of various waves reflected at the free surface of a size-dependent, pre-stressed, microstructured thermoelastic solid with a dual porosity framework. A generalized MooreGibsonThomson (MGT) model, incorporating higher-order terms and memory effects, is adopted to describe the complex heat transfer behavior within the material. A nonlocal framework based on Eringen's theory is utilized to derive the basic relations of the considered medium. An examination of the non-dimensionalized governing equations is conducted employing the normal mode technique to provide accurate solutions. The research demonstrates the presence of six separate wave modes that travel at varying speeds within the medium. The energy and amplitude ratios of reflected waves are determined by applying suitable boundary conditions. The influence of varying incidence angles on the reflected wave energy distribution is investigated numerically and visualized using MATLAB software. The study reveals that the energy ratios of the reflected waves are sensitive to the fractional-order parameter, kernel functions, initial stress, and nonlocality parameter. The analysis suggests a conservative reflection process, indicating minimal energy loss during reflection. Key findings and their implications for relevant scenarios are presented in the conclusion. Comparisons with existing models for certain cases demonstrate good agreement, supporting the validity of the present model. 2025 Elsevier Masson SAS